Abstract
Characterizing anesthesia-induced alterations to brain network dynamics provides a powerful framework to understand the neural mechanisms of unconsciousness. To this end, increased attention has been directed at how anesthetic drugs alter the functional connectivity between brain regions as defined through neuroimaging. However, the effects of anesthesia on temporal dynamics at functional network scales is less well understood. Here, we examine such dynamics in view of the free-energy principle, which postulates that brain dynamics tend to promote lower energy (more organized) states. We specifically engaged the hypothesis that such low-energy states play an important role in maintaining conscious awareness. To investigate this hypothesis, we analyzed resting-state BOLD fMRI data from human volunteers during wakefulness and under sevoflurane general anesthesia. Our approach, which extends an idea previously used in the characterization of neuron-scale populations, involves thresholding the BOLD time series and using a normalized Hamiltonian energy function derived from the Ising model. Our major finding is that the brain spends significantly more time in lower energy states during eyes-closed wakefulness than during general anesthesia. This effect is especially pronounced in networks thought to be critical for maintaining awareness, suggesting a crucial cognitive role for both the structure and the dynamical landscape of these networks.

Abstract
The identification of abnormal cognitive decline at an early stage becomes an increasingly significant conundrum to physicians and is of major interest in the studies of mild cognitive impairment (MCI). Support vector machine (SVM) as a high-dimensional pattern classification technique is widely employed in neuroimaging research. However, the application of a single SVM classifier may be difficult to achieve the excellent classification performance because of the small-sample size and noise of imaging data. To address this issue, we propose a novel method of the weighted random support vector machine cluster (WRSVMC) in which multiple SVMs were built and different weights were given to corresponding SVMs with different classification performances. We evaluated our algorithm on resting state functional magnetic resonance imaging (RS-fMRI) data of 93 MCI patients and 105 healthy controls (HC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The maximum accuracy given by the WRSVMC is 87.67%, demonstrating excellent diagnostic power. Furthermore, the most discriminative brain areas have been found out as follows: gyrus rectus (REC.L), precentral gyrus (PreCG.R), olfactory cortex (OLF.L), and middle occipital gyrus (MOG.R). These findings of the paper provide a new perspective for the clinical diagnosis of MCI.

Abstract
Early diagnosis remains a significant challenge for many neurological disorders, especially for rare disorders where studying large cohorts is not possible. A novel solution that investigators have undertaken is combining advanced machine learning algorithms with resting-state functional Magnetic Resonance Imaging to unveil hidden pathological brain connectome patterns to uncover diagnostic and prognostic biomarkers. Recently, state-of-the-art deep learning techniques are outperforming traditional machine learning methods and are hailed as a milestone for artificial intelligence. However, whole brain classification that combines brain connectome with deep learning has been hindered by insufficient training samples. Inspired by the transfer learning strategy employed in computer vision, we exploited previously collected resting-state functional MRI data for healthy subjects from existing databases and transferred this knowledge for new disease classification tasks. We developed a deep transfer learning neural network (DTL-NN) framework for enhancing the classification of whole brain functional connectivity patterns. Briefly, we trained a stacked sparse autoencoder (SSAE) prototype to learn healthy functional connectivity patterns in an offline learning environment. Then, the SSAE prototype was transferred to a DTL-NN model for a new classification task. To test the validity of our framework, we collected resting-state functional MRI data from the Autism Brain Imaging Data Exchange (ABIDE) repository. Using autism spectrum disorder (ASD) classification as a target task, we compared the performance of our DTL-NN approach with a traditional deep neural network and support vector machine models across four ABIDE data sites that enrolled at least 60 subjects. As compared to traditional models, our DTL-NN approach achieved an improved performance in accuracy, sensitivity, specificity and area under receiver operating characteristic curve. These findings suggest that DTL-NN approaches could enhance disease classification for neurological conditions, where accumulating large neuroimaging datasets has been challenging.

Abstract
The study of resting-state functional brain networks is a powerful tool to understand the neurological bases of a variety of disorders such as Autism Spectrum Disorder (ASD). In this work, we have studied the differences in functional brain connectivity between a group of 74 ASD subjects and a group of 82 typical-development (TD) subjects using functional magnetic resonance imaging (fMRI). We have used a network approach whereby the brain is divided into discrete regions or nodes that interact with each other through connections or edges. Functional brain networks were estimated using the Pearson's correlation coefficient and compared by means of the Network-Based Statistic (NBS) method. The obtained results reveal a combination of both overconnectivity and underconnectivity, with the presence of networks in which the connectivity levels differ significantly between ASD and TD groups. The alterations mainly affect the temporal and frontal lobe, as well as the limbic system, especially those regions related with social interaction and emotion management functions. These results are concordant with the clinical profile of the disorder and can contribute to the elucidation of its neurological basis, encouraging the development of new clinical approaches.

Abstract
OBJECTIVE: This study determined the clinical utility of an fMRI classification algorithm predicting medication-class of response in patients with challenging mood diagnoses.
METHODS: Ninety-nine 16-27-year-olds underwent resting state fMRI scans in three groups-BD, MDD and healthy controls. A predictive algorithm was trained and cross-validated on the known-diagnosis patients using maximally spatially independent components (ICs), constructing a similarity matrix among subjects, partitioning the matrix in kernel space and optimizing support vector machine classifiers and IC combinations. This classifier was also applied to each of 12 new individual patients with unclear mood disorder diagnoses.
RESULTS: Classification within the known-diagnosis group was approximately 92.4% accurate. The five maximally contributory ICs were identified. Applied to the complicated patients, the algorithm diagnosis was consistent with optimal medication-class of response to sustained recovery in 11 of 12 cases (i.e., almost 92% accuracy).
CONCLUSION: This classification algorithm performed well for the know-diagnosis but also predicted medication-class of response in difficult-to-diagnose patients. Further research can enhance this approach and extend these findings to be more clinically accessible.

Abstract
The default mode network (DMN) is an important connectivity hub, and alterations may play a role in the pathophysiology of several neuropsychiatric disorders. Despite the growing body of research on DMN (dys)function, the underlying neurochemical substrate remains to be elucidated. The serotonergic neurotransmitter system has been suggested to play a substantial role in modulating the DMN. Therefore, we investigated the association between serotonin transporter (SERT) occupancy by the selective serotonin reuptake inhibitor citalopram and DMN functional connectivity. Forty-five healthy female volunteers (mean age = 21.6y) participated in a double-dose study. The subjects were randomized to pre-treatment with placebo, a low (4 mg; 'low group') or clinically standard (16 mg; 'high group') oral citalopram dose (corresponding to 0%, ∼40% and ∼80% SERT occupancy, respectively). They underwent [123I]FP-CIT single-photon emission computed tomography (SPECT) imaging to assess SERT occupancy. In addition, resting-state functional magnetic resonance imaging was used to measure DMN connectivity. With non-parametric permutation testing we assessed the association between SERT occupancy and DMN connectivity. We found that SERT occupancy by citalopram was negatively associated with DMN connectivity with a number of cortical regions, including the anterior cingulate cortex (ACC), paracingulate gyrus, postcentral gyrus, superior parietal gyrus and temporal pole. These findings provide further neurochemical evidence that the serotonin system dose-dependently modulates DMN function.

Abstract
OBJECTIVE: A growing body of preclinical and clinical literature suggests that brain-gut-microbiota interactions play an important role in human health and disease, including hedonic food intake and obesity. We performed a tripartite network analysis based on graph theory to test the hypothesis that microbiota-derived fecal metabolites are associated with connectivity of key regions of the brain's extended reward network and clinical measures related to obesity.
METHODS: DTI and resting state fMRI imaging was obtained from 63 healthy subjects with and without elevated body mass index (BMI) (29 males and 34 females). Subjects submitted fecal samples, completed questionnaires to assess anxiety and food addiction, and BMI was recorded.
RESULTS: The study results demonstrate associations between fecal microbiota-derived indole metabolites (indole, indoleacetic acid, and skatole) with measures of functional and anatomical connectivity of the amygdala, nucleus accumbens, and anterior insula, in addition to BMI, food addiction scores (YFAS) and anxiety symptom scores (HAD Anxiety).
CONCLUSIONS: The findings support the hypothesis that gut microbiota-derived indole metabolites may influence hedonic food intake and obesity by acting on the extended reward network, specifically the amygdala-nucleus accumbens circuit and the amygdala-anterior insula circuit. These cross sectional, data-driven results provide valuable information for future mechanistic studies.

Abstract
In contrast to most existing studies that typically characterize the developmental sex differences using analysis of variance or equivalently multiple linear regression, we present a parameter-free centralized multi-task learning method to identify sex specific and common resting state functional connectivity (RSFC) patterns underlying the brain development based on resting state functional MRI (rs-fMRI) data. Specifically, we design a novel multi-task learning model to characterize sex specific and common RSFC patterns in an age prediction framework by regarding the age prediction for males and females as separate tasks. Moreover, the importance of each task and the balance of these two patterns, respectively, are automatically learned in order to make the multi-task learning robust as well as free of tunable parameters, i.e., parameter-free for short. Our experimental results on synthetic datasets verified the effectiveness of our method with respect to prediction performance, and experimental results on rs-fMRI scans of 1041 subjects (651 males) of the Philadelphia Neurodevelopmental Cohort (PNC) showed that our method could improve the age prediction on average by 5.82% with statistical significance than the best alternative methods under comparison, in addition to characterizing the developmental sex differences in RSFC patterns.

BRAIN AGE PREDICTION BASED ON RESTING-STATE FUNCTIONAL CONNECTIVITY PATTERNS USING CONVOLUTIONAL NEURAL NETWORKS.

Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:101-104

Authors: Li H, Satterthwaite TD, Fan Y

Abstract
Brain age prediction based on neuroimaging data could help characterize both the typical brain development and neuropsychiatric disorders. Pattern recognition models built upon functional connectivity (FC) measures derived from resting state fMRI (rsfMRI) data have been successfully used to predict the brain age. However, most existing studies focus on coarse-grained FC measures between brain regions or intrinsic connectivity networks (ICNs), which may sacrifice fine-grained FC information of the rsfMRI data. Whole brain voxel-wise FC measures could provide fine-grained FC information of the brain and may improve the prediction performance. In this study, we develop a deep learning method to use convolutional neural networks (CNNs) to learn informative features from the fine-grained whole brain FC measures for the brain age prediction. Experimental results on a large dataset of resting-state fMRI demonstrate that the deep learning model with fine-grained FC measures could better predict the brain age.

Abstract
Biomarkers of inflammation, including inflammatory cytokines and the acute-phase reactant C-reactive protein (CRP), are reliably increased in a subset of patients with depression, anxiety disorders and post-traumatic stress disorder (PTSD). Administration of innate immune stimuli to laboratory subjects and the associated release of inflammatory cytokines has been shown to affect brain regions involved in fear, anxiety and emotional processing such as the amygdala. However, the role of inflammation in altered circuitry involving amygdala and other brain regions and its subsequent contribution to symptom severity in depression, anxiety disorders and PTSD is only beginning to be explored. Herein, medically-stable, currently unmedicated outpatients with a primary diagnosis of major depressive disorder (MDD; n=48) underwent resting-state functional MRI (rfMRI) to determine whether altered connectivity between the amygdala and whole brain was observed in a subset of patients with high inflammation and symptoms of anxiety. Whole-brain, voxel-wise functional connectivity analysis of the right and left amygdala as a function of inflammation (plasma CRP concentrations) revealed that increased CRP predicted decreased functional connectivity between right amygdala and left ventromedial prefrontal cortex (vmPFC) (corrected p<0.05). Amygdala-vmPFC connectivity was, in turn, negatively correlated with symptoms of anxiety (r=-0.33, df=46, p=0.022). In exploratory analyses, relationships between low amygdala-vmPFC connectivity and high anxiety were only observed in patients with a secondary diagnosis of an anxiety disorder or PTSD (r=-0.54 to -0.87, p<0.05). More work is needed to understand the role of inflammation and its effects on amygdala-vmPFC circuitry and symptoms of anxiety in MDD patients with comorbid anxiety disorders or PTSD.

Abstract
Multiple sclerosis (MS) patients present several alterations related to sensing of bodily signals. However, no specific neurocognitive impairment has yet been proposed as a core deficit underlying such symptoms. We aimed to determine whether MS patients present changes in interoception-that is, the monitoring of autonomic bodily information-a process that might be related to various bodily dysfunctions. We performed two studies in 34 relapsing-remitting, early-stage MS patients and 46 controls matched for gender, age, and education. In Study 1, we evaluated the heartbeat-evoked potential (HEP), a cortical signature of interoception, via a 128-channel EEG system during a heartbeat detection task including an exteroceptive and an interoceptive condition. Then, we obtained whole-brain MRI recordings. In Study 2, participants underwent fMRI recordings during two resting-state conditions: mind wandering and interoception. In Study 1, controls exhibited greater HEP modulation during the interoceptive condition than the exteroceptive one, but no systematic differences between conditions emerged in MS patients. Patients presented atrophy in the left insula, the posterior part of the right insula, and the right anterior cingulate cortex, with abnormal associations between neurophysiological and neuroanatomical patterns. In Study 2, controls showed higher functional connectivity and degree for the interoceptive state compared with mind wandering; however, this pattern was absent in patients, who nonetheless presented greater connectivity and degree than controls during mind wandering. MS patients were characterized by atypical multimodal brain signatures of interoception. This finding opens a new agenda to examine the role of inner-signal monitoring in the body symptomatology of MS.

Abstract
Neuroimaging research made rapid advances in the study of the functional architecture of the brain during the past decade. Many proposals endorsed the relevance of large-scale brain networks, defined as ensembles of brain regions that exhibit highly correlated signal fluctuations. However, analysis methods need further elaboration to define the functional and anatomical extent of specialized subsystems within classical networks with a high reliability. We present a novel approach to characterize and examine the functional proprieties of brain networks. This approach, labeled as brain network profiling (BNP), considers similarities in task-evoked activity and resting-state functional connectivity across biologically relevant brain subregions. To combine task-driven activity and functional connectivity features, principal components were extracted separately for task-related beta values and resting-state functional connectivity z-values (data available from the Human Connectome Project), from 360 brain parcels. Multiple clustering procedures were employed to assess if different clustering methods (Gaussian mixtures; k-means) and/or data structures (task and rest data; only rest data) led to improvements in the replication of the brain architecture. The results indicated that combining information from resting-state functional connectivity and task-evoked activity and using Gaussian mixtures models for clustering produces more reliable results (99% replication across data sets). Moreover, the findings revealed a high-resolution partition of the cerebral cortex in 16 networks with unique functional connectivity and/or task-evoked activity profiles. BNP potentially offers new approaches to advance the investigation of the brain functional architecture.

Abstract
BACKGROUND: Amyloid pathology is the pathological hallmark in Alzheimer's disease (AD) and can precede clinical dementia by decades. So far it remains unclear how amyloid pathology leads to cognitive impairment and dementia. To design AD prevention trials it is key to include cognitively normal subjects at high risk for amyloid pathology and to find predictors of cognitive decline in these subjects. These goals can be accomplished by targeting twins, with additional benefits to identify genetic and environmental pathways for amyloid pathology, other AD biomarkers, and cognitive decline.
METHODS: From December 2014 to October 2017 we enrolled cognitively normal participants aged 60 years and older from the ongoing Manchester and Newcastle Age and Cognitive Performance Research Cohort and the Netherlands Twins Register. In Manchester we included single individuals, and in Amsterdam monozygotic twin pairs. At baseline, participants completed neuropsychological tests and questionnaires, and underwent physical examination, blood sampling, ultrasound of the carotid arteries, structural and resting state functional brain magnetic resonance imaging, and dynamic amyloid positron emission tomography (PET) scanning with [18F]flutemetamol. In addition, the twin cohort underwent lumbar puncture for cerebrospinal fluid collection, buccal cell collection, magnetoencephalography, optical coherence tomography, and retinal imaging.
RESULTS: We included 285 participants, who were on average 74.8 ± 9.7 years old, 64% female. Fifty-eight participants (22%) had an abnormal amyloid PET scan.
CONCLUSIONS: A rich baseline dataset of cognitively normal elderly individuals has been established to estimate risk factors and biomarkers for amyloid pathology and future cognitive decline.

Abstract
Although the amygdala's role in shaping social behavior is especially important during early post-natal development, very little is known of amygdala functional development before childhood. To address this gap, this study uses resting-state fMRI to examine early amygdalar functional network development in a cross-sectional sample of 80 children from 3-months to 5-years of age. Whole brain functional connectivity with the amygdala, and its laterobasal and superficial sub-regions, were largely similar to those seen in older children and adults. Functional distinctions between sub-region networks were already established. These patterns suggest many amygdala functional circuits are intact from infancy, especially those that are part of motor, visual, auditory and subcortical networks. Developmental changes in connectivity were observed between the laterobasal nucleus and bilateral ventral temporal and motor cortex as well as between the superficial nuclei and medial thalamus, occipital cortex and a different region of motor cortex. These results show amygdala-subcortical and sensory-cortex connectivity begins refinement prior to childhood, though connectivity changes with associative and frontal cortical areas, seen after early childhood, were not evident in this age range. These findings represent early steps in understanding amygdala network dynamics across infancy through early childhood, an important period of emotional and cognitive development.

Abstract
Human adolescence is a period of rapid changes in cognition and goal-directed behavior, and it constitutes a major transitional phase towards adulthood. One of the mechanisms suggested to underlie the protracted maturation of functional brain networks, is the increased network integration and segregation enhancing neural efficiency. Importantly, the increasing coordinated network interplay throughout development is mediated through functional hubs, which are highly connected brain areas suggested to be pivotal nodes for the regulation of neural activity. To elucidate brain hub development during childhood and adolescence, we estimated voxel-wise eigenvector centrality (EC) using functional magnetic resonance imaging (fMRI) data from two different psychological contexts (resting state and a working memory task), in a large cross-sectional sample (n = 754) spanning the age from 8 to 22 years, and decomposed the maps using independent component analysis (ICA). Our results reveal significant age-related centrality differences in cingulo-opercular, visual, and sensorimotor network nodes during both rest and task performance, suggesting that common neurodevelopmental processes manifest across different mental states. Supporting the functional significance of these developmental patterns, the centrality of the cingulo-opercular node was positively associated with task performance. These findings provide evidence for protracted maturation of hub properties in specific nodes of the brain connectome during the course of childhood and adolescence and suggest that cingulo-opercular centrality is a key factor supporting neurocognitive development.

Abstract
Brain imaging studies indicate that chronic cocaine users display altered functional connectivity between prefrontal cortical, thalamic, striatal, and limbic regions; however, the use of cross-sectional designs in these studies precludes measuring baseline brain activity prior to cocaine use. Animal studies can circumvent this limitation by comparing functional connectivity between baseline and various time points after chronic cocaine use. In the present study, adult male Long-Evans rats were trained to self-administer cocaine intravenously for 6 h sessions daily over 14 consecutive days. Two additional groups serving as controls underwent sucrose self-administration or exposure to the test chambers alone. Functional magnetic resonance imaging was conducted before self-administration and after 1 and 14 d of abstinence (1d and 14d Abs). After 1d Abs from cocaine, there were increased clustering coefficients in brain areas involved in reward seeking, learning, memory, and autonomic and affective processing, including amygdala, hypothalamus, striatum, hippocampus, and thalamus. Similar changes in clustering coefficient after 1d Abs from sucrose were evident in predominantly thalamic brain regions. Notably, there were no changes in strength of functional connectivity at 1 or 14 d after either cocaine or sucrose self-administration. The results suggest that cocaine and sucrose can change the arrangement of functional connectivity of brain regions involved in cognition and emotion, but that these changes dissipate across the early stages of abstinence. The study also emphasizes the importance of including baseline measures in longitudinal functional neuroimaging designs seeking to assess functional connectivity in the context of substance use.